Advancing AI-Powered Assistive Technologies with Tripolar Fuzzy Integration: Enhancing Decision-Making for Individuals with Disabilities

Assistive technologies are innovative solutions designed to mitigate barriers faced by individuals with disabilities, empowering them to participate more fully in various aspects of life. These technologies span across different disability categories, offering tailored support to address specific challenges. The convergence of assistive technologies and artificial intelligence (AI) has led to transformative solutions for individuals with disabilities, providing a variety of innovative functionalities to improve their quality of life. AI techniques can be employed to process data and facilitate decision-making. Fuzzy sets are crucial in AI, but traditional fuzzy sets are binary in nature, assigning degrees of membership to an element and its complement. However, many decision-making scenarios require expressing a level of hesitation or uncertainty regarding the degree of membership. A tripolar fuzzy set (TFS) introduces a third value, enabling the explicit representation of hesitation or neutrality. This hybrid structure combines the information from both bipolar fuzzy sets and intuitionistic fuzzy sets. In real-world problems, TFS is a more effective tool for handling ambiguity. In this paper, the integration of TFS and AI is proposed as a promising approach to enhance the effectiveness of AI, offering personalized and effective solutions for individuals with diverse challenges. Several aggregation operators for tripolar fuzzy information are explored. We develop the tripolar fuzzy weighted geometric, tripolar fuzzy hybrid geometric, tripolar fuzzy ordered weighted geometric, tripolar fuzzy weighted averaging, tripolar fuzzy hybrid weighted averaging, and tripolar fuzzy ordered weighted averaging operator. We present the fundamental characteristics of the proposed operators and have developed a multicriteria decision-making algorithm based on tripolar fuzzy aggregation operators. We apply the proposed MCDM model to solve of the numerical example of the evaluation of AI-powered assistive technologies for disable individuals. Finally, we compare our developed technique to various current approaches in order to validate the proposed method and demonstrate its usefulness and efficacy.